Evidence map›Paper›PMID 39317458›Full record

ArticleCNS neuroscience & therapeutics2024

Expression of microRNA induced by postoperative delirium-like behavior is associated with long-term default mode network disruption: Sequencing and a secondary analysis of resting-state fMRI data.

Yang Liu, Huiru Feng, Huiqun Fu, Binbin Nie, Tianlong Wang

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Article in CNS neuroscience & therapeutics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yang LiuDepartment of Anesthesiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Huiru FengDepartment of Anesthesiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Huiqun FuDepartment of Anesthesiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Binbin NieInstitute of High Energy Physics, Chinese Academy of Sciences, Beijing, China.
Tianlong WangDepartment of Anesthesiology, Xuanwu Hospital, Capital Medical University, Beijing, China.ORCID 0000-0001-6291-2505

Funding

Capital's Funds for Health Improvement and Research CFH2024-4-20114Post-subsidy funds for National Clinical Research Center, Ministry of Science and Technology of China 303-01-001-0272-03Training Fund for Open Projects at Clinical Institutes and Departments of Capital Medical University CCMU2023ZKYXY006
6 · The paper itself

Abstract

aimsResting state functional magnetic resonance imaging (rs-fMRI) has been widely used in studying default mode network (DMN) changes in postoperative delirium (POD). Reproducibility and interpretability of the analyzing results remain insufficiently studied.

methodsDelirium-like behavior was induced by tibial fixation surgery under isoflurane anesthesia. Firstly, we evaluated delirium-like behavior and inflammatory responses in hippocampus and systemic level. Then the expressions of microRNA (miRNA) and target gene were sequenced and validated. Afterwards the functional connectivity (FC) in DMN was analyzed. Finally, results were correlated with DMN changes.

resultsPOD-like behavior caused significant changes of miR-34b-5p, miR-328-5p, and miR-3505 in miRNA level and Nos1, Tubb3, and Gys1 in the gene level. The FC in left and right hippocampus (L-Hip and R-Hip) and right auditory cortex (R-AC) was found significantly changed. Significant correlations were found in FC

conclusionOur study demonstrates that POD-like behavior induced significant miRNA and gene expression changes were associated with hippocampus related long-term FC disruption in DMN. The results increased reproducibility and interpretability for standardized rs-fMRI data analysis, as well as providing potential targets for postoperative delirium treatment.

Indexed as

Default Mode NetworkMagnetic Resonance ImagingMicroRNAsAnimalsDeliriumFemaleHippocampusHumansMalePostoperative ComplicationsRestMicroRNAsdefault mode networkmicroRNAneuroinflammationpostoperative deliriumresting state functional magnetic resonance imaging

Identifiers

PMID39317458
PMCPMC11421890

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.